Search Archive

30.6.11

Man, I wish my real neuroscience research got as much attention as my fake science! What does that say about my quality of work, eh? :D

So once again serendipity has reared its lovely head. Actually, that's unfair, I wouldn't call what's happened here "luck", as there's been a lot of leg-work and networking that gotten this stuff out there, but anyway...

Well, someone working with Pimsleur saw the video and Quora response and shot me an email asking if he could create an infographic on how our "results" inform ways in which we could survive the zombie apocalypse.

In the mean time I'd been in touch with Angela over at Wired about doing something zombie-related, and this seemed like the perfect opportunity.

I've talked at length on this blog about why I do the zombie neuroscience stuff: it's a tongue-in-cheek jab at cognitive neuroscience, it gets people to accidentally learn about the brain, and it's just damn excellent geeky fun!

29.6.11

Just a quick note: I'm up in Quebec City for the 17th annual Organization for Human Brain Mapping conference (OHBM, or just "HBM"). This is my first time at this conference, since it's usually considered to be an (f)MRI conference, which isn't really my thing.

Speaking of brainSCANr, Tal Yarkoni just released the public beta version of his neuroimaging meta-analytics tool, neurosynth just in time for OHBM! It's an amazing piece of technology recently published in Nature Methods, so you should check it out.

Also, this conference is my wife Jessica'sfirst publication at a scientific meeting, so please everyone give her a round of applause! She did all of the system design, visualization, etc. for brainSCANr.

We've delayed the paper to work on what I'm calling "semi-automated hypothesis generation". We've also been working on integrating our findings with gene expression data from the Allen Brain Atlas (ABA).

So I spilled the beans on Quora last week about what we've been up to on brainSCANr. Someone asked a series of brainSCANr questions that I answered. It was pretty cool to see then on there actually. I was pleased!

In one of my responses on Quora, I explain the hypothesis generation method a bit:

Note this is preliminary, but the idea is to find associations that are disproportionally ranked to find possible missing research associations. In the example above, for instance, "migraine" is most strongly associated with "serotonin", but "serotonin" is relatively weakly associated with "migraine" compared to how much more strongly it's associated with other terms such as the raphe nuclei, anxiety, etc. By using this calculation metric, we are attempting to find "holes" in the scientific literature, and trying to infer possible new or under-studied paths of research.

I also explain the integration with the ABA:

In the image above, you can see the actual gene expression profile for a serotonin receptor gene (HTR1A). As expected, this gene has a very high expression profile in the raphe nuclei, which brainSCANr tells us is the brain region most strongly associated with serotonin.

Interestingly, however, the raphe nuclei are not the brain region that most strongly expresses the HTR1A gene. In fact, the region that does so is the zona incerta, a brain region that is very weakly associated with serotonin.

In fact, at the time of this writing, the zona incerta has two orders of magnitude fewer co-papers with serotonin than do the raphe nuclei (42 papers versus 4482).

This suggests that there is an overemphasis in the literature on the raphe nuclei and serotonin, and a serious dearth of information about the zona incerta.

We believe the combined information between the ABA and brainSCANr will offer some exciting insights.

Anyway, if you're at the conference, come drop by my poster on Wednesday or Thursday. I'll be in front of the poster from 10:30am onward Thursday morning, but sadly, due to the fact that she's 30-something weeks pregnant, my wife couldn't make the fight out to present with me.

This post is getting long, so I'll cut short my thoughts on Quebec and just post a few pictures and a video. The video is from the amazing (and free!) Cirque du Soleil show, Les Chemins Invisibles. This show is performed outside under a highway overpas for free during the summer in Quebec City. Incredible.

My plane from Montreal to Quebec was super tiny. May have been the smallest plane I've been on:

The conference has a cool art section that I'm really happy to see:

And the old city itself is very cool... feels very Victorian European:

There was one I'd been wanting to write for a while but hadn't gotten around to because of the amount of work that it would require. Well I finally got around to it, and it's posted over on SciAm. It's the most proper "news article-like" thing I've written.

I also uploaded a video of his original echolocation experiments from 1940:

I had to edit out a few pieces of the video from the original Galambos version, but I honestly didn't cut anything critical. I left in all of his narration. The only parts I cut were the repetitions showing the bats flying normally, bats flying awkwardly, etc. He had several extra minutes of these segments to help demonstrate his point.

As for my article, I'd appreciate it if you all would read the whole thing, mainly because I'd love to get your feedback and thoughts on it!

Here's one of my favorite excerpts:

What impressed me the most about Galambos though wasn't his superb research, but rather the extent to which he'd sacrifice himself for his pursuit of knowledge.

In another experiment, conducted in response to the bombing of Pearl Harbor in 1942, Galambos and hearing researcher Hallowell "Hal" Davis were asked to "find out how much and what kind of sound it takes to injure or incapacitate a man."

How did they test this? Galambos explains:

we proceeded to expose our ears to the sound waves emitted by a so-called bullhorn, the kind of loudspeaker the Navy used to deliver messages to personnel wearing earplugs on the busy flight deck of an aircraft carrier. We systematically varied the three sound variables--intensity, frequency, and duration--producing in ourselves increasingly larger temporary hearing losses, until we neared combinations we thought might cause a permanent loss. At the end of the project, Hal decided to find out if our predictions were correct, and told us to expose his right ear--we always protected his left ear--to a wideband noise at 130 dB for 32 minutes. As predicted, this exposure permanently sliced a few hundred Hz off the high end of his existing congenital hearing loss in the 3500-3800 Hz region.

They partially deafened themselves. For science!

I'm excited about this piece, but it was a lot of work, so I don't think I'll be writing these kinds of pieces often.

Next, as part of my work with the Zombie Research Society, I'll be speaking on a panel called "History of the Modern Zombie" at this year's Comic-Con. I've been going to Comic-Con since I was about 12, so I'm really excited about being a part of this.

The other piece of big news is that my post-doctoral grant got funded, which means starting this Fall I will be employed at UCSF working in Adam Gazzaley's lab. This guarantees me three years of funding, which is great. I'm going to continue my research on the role that neuroplasticity plays in cognition. While I'd love to tell more, I'm being intentionally cagey about the details right now as we're already considering publishing a paper based on some of our results.

But it will be a few months before I start at UCSF, so I've got a few free months.

In the meantime I'll be working with one of my oldest friends, Curtis Chambers, at the startup where he works in San Francisco: Uber (New York Times coverage here and here; Wired here). In short, it's an on-demand car service where you reserve a black-car via an iPhone or Android app, or via SMS.

I'm working as their computational scientist (aka, "data pornographer") working on internal tools and analytics as well as writing a public-facing blog. Our goal is to use our historical data to more accurately estimate and reduce pick-up times.

My first thought about making the academia > startup jump is best summed up by Dr. Ray Stantz:

For those of you who know me, or who saw my Google Tech Talk, you know how important a role I believe data will play in the future of science. Part of that will be sophisticated data mining and analytics, and that will be complemented by data visualization.

brainSCANr was my first attempt at bringing neuroscience into the data-driven future. By working closely with the tech community, I hope to gain new skills and connections that will help me go farther.

The data visualization and analysis I'm doing at Uber has been really fun; the data we're collecting here is huge. It's very data-driven. It's seriously impressive, and I think that neuroscientists (and academics in general) could learn a lot by interfacing with tech companies.

For example, check out the map I made of where all of our cars have been in San Francisco:

This past weekend my wife and I were driving around and I was geeking out about some of the analytics that we're working on at Uber. Her response was (to paraphrase), "it's amazing what happens when you put a bunch of nerds in a room together; somehow you guys are making getting a car to pick you up an interesting problem".

I'm used to working on problems deeply and slowly. That is, in academia we only publish when everything's in a relatively final form. The trick with a startup is rapid, incremental improvements. This is very different from academia (though I will gladly rant about how much I think academia could benefit from working with startups and data companies...)

More and more I have become interested in the data collection > analysis > visualization > interpretation workflow, and how technological improvements can help neuroscience research.

For the most part scientists and researchers, by definition, have very specific domain knowledge. But neuroscience in particular is a field that requires more knowledge than any one person can integrate: biology, chemistry, mathematics, philosophy, linguistics, psychology, and so on.

The farther along the field gets, the more tools will be required for data analysis and visualization of brain data. That is, the field badly needs maps and analytics tools. Statistics and programming are becoming critical components to assist in the neuroscience research process.

Which is exactly what I'm doing for Uber.

I'm building geo-location and analytics tools using python and js. It's amazing how much optimization and analysis can be done using the data we've collected. As Henry's post shows, every ride you take makes us smarter. Mapping the flow of a city like San Francisco or New York is amazing.

At the same time, I'm in an environment with amazing coders and thinkers, learning new tools that I can then turn around and apply to my research.

Social/RSS

Follow by Email

Who I Am

Neuroscientist combining large scale data-mining, machine-learning techniques, and brain computer interfacing with hypothesis-driven experimental research to understand the relationships between the human frontal lobes, cognition, and disease. Into really geeky stuff. World zombie neuroscience expert. Also run brainSCANr.com with my wife, Jessica.